model_base_keras.py 文件源码

def build_keras_model(self):
        ''' #example:
        from keras import layers
        from keras import models
        from keras import optimizers
        input_ = layers.Input(shape=(self.input_dims_,))
        model = layers.Dense(256, kernel_initializer='Orthogonal')(input_)
        #model = layers.BatchNormalization()(model)
        model = layers.Activation('selu')(model)
        #model = layers.noise.AlphaDropout(0.2, seed=1)(model)
        #model = layers.advanced_activations.PReLU()(model)
        #model = layers.Dropout(0.4)(model)

        model = layers.Dense(64, kernel_initializer='Orthogonal')(model)
        #model = layers.BatchNormalization()(model)
        model = layers.Activation('selu')(model)
        #model = layers.noise.AlphaDropout(0.1, seed=1)(model)
        #model = layers.advanced_activations.PReLU()(model)
        #model = layers.Dropout(0.4)(model)

        model = layers.Dense(16, kernel_initializer='Orthogonal')(model)
        #model = layers.BatchNormalization()(model)
        model = layers.Activation('selu')(model)
        #model = layers.advanced_activations.PReLU()(model)

        model = layers.Dense(1, activation='sigmoid')(model)

        model = models.Model(input_, model)
        model.compile(loss = 'binary_crossentropy', optimizer = optimizers.Nadam())
        #print(model.summary(line_length=120))
        return model
        '''
        raise Exception('implement this!')

    #@tf_force_cpu